We also compared our method with a set of context-aware approaches explored in a systemic study on context-aware learning by Sirinukunwattana et al. [1]}.
Ten different approaches (A-J) were considered to capture contextual information. For a detailed description of these methods please see Figure 2 of [1]}. It can be observed that these approaches have significant overlap with each other. some of these approaches differ only in term of input patch resolution, as some of these used shared weights for different networks.
First four (A-D) approaches try to capture the context by down-sampling the high-resolution images at different magnification levels e.g. \(20\times , 10\times , 5\times , \)  and \( 2.5\times \) . Channel-wise concatenated four multi-resolution images as an input of a CNN are consider in approach E. However, approaches F and I concatenate the CNN features of four multi-resolution input images in vector form before prediction. The full image at \(20\times \)  magnification is used as an input in approach H. This approach is not feasible in case of an image with very large spatial dimensions due to memory constraints. Approaches G and J use LSTM to capture the context from the CNN features of four multi-resolution input images. The code of method G is publicly available by the authors of [1]} and we use that code to retrain the LSTM based method on CRC dataset for a fair comparison. Our method outperformed their context-aware method [1]} as well for both binary and three-class CRC grading (Table REF ).
